Real-time hysteresis identification in structures based on restoring force reconstruction and Kalman filter
Real-time hysteresis identification in structures based on restoring force reconstruction and Kalman filter
复制标题
基于恢复力重构和卡尔曼滤波器的结构实时滞后识别
DOI：
10.1016/j.ymssp.2020.107297
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发表时间：
2021-03
影响因子：
8.4
通讯作者：
Takewaki Izuru
中科院分区：
文献类型：
--
作者：
Wang Li;Guo Jia;Takewaki Izuru
识别结构的局部非线性滞回特性是结构健康监测中的一个重要问题。本文提出了一种新的滞回识别框架，其中，而不是识别的滞回参数，恢复力的重建，以便识别滞回曲线。在这种新的框架下，有没有需要得到的滞后模型的先验知识，使更广泛的应用，所涉及的系统方程是线性的未知恢复力，趋于更有效的。然后，采用卡尔曼滤波器进行实时恢复力重建，因为卡尔曼滤波器在有噪声的测量数据的线性系统估计中具有高效、有效和可靠性。在这样做时，恢复力被额外地增强为状态变量，并且这样的增强被示出作为吉洪诺夫正则化的作用，通过该正则化参数的增强的适当的协方差通过L-曲线方法被选择。数值算例和实验验证了所提出的实时迟滞识别方法的有效性和鲁棒性。
Identifying the local nonlinear hysteretic behaviors in structures arises as an important issue in structural health monitoring. This paper develops a new hysteresis identification framework where rather than identifying the hysteretic parameters, the restoring forces are reconstructed so as to identify the hysteretic loops. Under this new framework, there is no need to get a priori knowledge on the hysteretic models, enabling a wider range of applications, and the involved system equation is linear with unknown restoring forces, tending to be more efficient. Then, the Kalman filter is adopted for real-time restoring force reconstruction because the Kalman filter is celebrated for its efficiency, effectiveness and reliability in linear system estimation with noisy measured data. In doing so, the restoring forces are additionally augmented as state variables and such augmentation is shown to act as the role of Tikhonov regularization, by which the proper covariance of the augmentation as the regularization parameter is selected via the L-curve method. Numerical examples are presented and an experimental test is conducted to verify the effectiveness and robustness of the proposed real-time hysteresis identification method.
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影响因子：
4.5
作者：
C. Katsaras;T. Panagiotakos;B. Kolias
通讯作者：
C. Katsaras;T. Panagiotakos;B. Kolias
影响因子：
4.1
作者：
F. Leckie
通讯作者：
F. Leckie
影响因子：
6.3
作者：
N. Vaiana;S. Sessa;F. Marmo;L. Rosati
通讯作者：
N. Vaiana;S. Sessa;F. Marmo;L. Rosati
DOI：
10.1061/(asce)0733-9399(1998)124:10(1059
发表时间：
1998-10
期刊：
Journal of Engineering Mechanics-asce
影响因子：
--
作者：
Y. Kitada
通讯作者：
Y. Kitada
影响因子：
5.4
作者：
Chatzis, Manolis N.;Chatzi, Eleni N.;Smyth, Andrew W.
通讯作者：
Smyth, Andrew W.